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Practical Time Series Analysis

You're reading from   Practical Time Series Analysis Master Time Series Data Processing, Visualization, and Modeling using Python

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781788290227
Length 244 pages
Edition 1st Edition
Languages
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Authors (2):
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Avishek Pal Avishek Pal
Author Profile Icon Avishek Pal
Avishek Pal
PKS Prakash PKS Prakash
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PKS Prakash
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Toc

Modeling higher-order exponential smoothing

The concept can be further extended to higher-order exponential smoothing with an nth order polynomial model:

Here, error εt ∼ N(0,σ2) is normally distributed with 0 mean and σ2 variance. The exponential smoothers used for higher order are as follows:

...

Here, is weights for smoothers. Usually, higher-order exponential smoothing is not used in time as even for second order smoothing, the computation becomes very hard and approaches such as Autoregressive Integrated Moving Average (ARIMA) are utilized. It will be further discussed in Chapter 4, Auto Regressive Models.

Another very popular exponential smoothing is triple exponential smoothing. The triple exponential smoothing allows you to capture seasonality with level and trend. The relationship between levels, trends, and seasonality is defined using the...

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